Transformers

New model: RoBERTa

**[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du et al.

Thanks to Myle Ott from Facebook for his help.

Tokenizer sequence pair handling

Tokenizers get two new methods:


tokenizer.add_special_tokens_single_sentence(token_ids)

and

tokenizer.add_special_tokens_sentences_pair(token_ids_0, token_ids_1)


These methods add the model-specific special tokens to sequences. The sentence pair creates a list of tokens with the `cls` and `sep` tokens according to the way the model was trained.

Sequence pair examples:

For BERT:


[CLS] SEQUENCE_0 [SEP] SEQUENCE_1 [SEP]


For RoBERTa:


<s> SEQUENCE_0 </s></s> SEQUENCE_1 </s>


Tokenizer encoding function
The tokenizer encode function gets two new arguments:


tokenizer.encode(text, text_pair=None, add_special_tokens=False)


If the `text_pair` is specified, `encode` will return a tuple of encoded sequences. If the `add_special_tokens` is set to `True`, the sequences will be built with the models' respective special tokens using the previously described methods.

AutoConfig, AutoModel and AutoTokenizer

There are three new classes with this release that instantiate one of the base model classes of the library from a pre-trained model configuration: `AutoConfig`, `AutoModel`, and `AutoTokenizer`.

Those classes take as input a pre-trained model name or path and instantiate one of the corresponding classes. The input string indicates to the class which architecture should be instantiated. If the string contains "bert", `AutoConfig` instantiates a `BertConfig`, `AutoModel` instantiates a `BertModel` and `AutoTokenizer` instantiates a `BertTokenizer`.

The same can be done for all the library's base models. The Auto classes check for the associated strings: "openai-gpt", "gpt2", "transfo-xl", "xlnet", "xlm" and "roberta". The documentation associated with this change can be found [here](https://huggingface.co/pytorch-transformers/model_doc/auto.html).

Examples

Some examples have been refactored to better reflect the current library. Those are: `simple_lm_finetuning.py`, `finetune_on_pregenerated.py`, as well as `run_glue.py` that has been adapted to the RoBERTa model. The examples `run_squad` and `run_glue.py` have better dataset processing with caching.

Bug fixes and improvements to the library modules

- Fixed multi-gpu training when using FP16 (zijunsun)
- Re-added the possibility to import BertPretrainedModel (thomwolf)
- Improvements to tensorflow -> pytorch checkpoints (dhpollack)
- Fixed save_pretrained to save the correct added tokens (joelgrus)
- Fixed version issues in run_openai_gpt (rabeehk)
- Fixed issue with line return with Chinese BERT (Yiqing-Zhou)
- Added more flexibility regarding the `PretrainedModel.from_pretrained` (xanlsh)
- Fixed issues regarding backward compatibility to Pytorch 1.0.0 (thomwolf)
- Added the unknown token to GPT-2 (thomwolf)

Name change: welcome PyTorch-Transformers 👾

`pytorch-pretrained-bert` => `pytorch-transformers`

Install with `pip install pytorch-transformers`

New models

- **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
- **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.

New pretrained weights

We went from ten (in `pytorch-pretrained-bert` 0.6.2) to twenty-seven (in `pytorch-transformers` 1.0) pretrained model weights.

The newly added model weights are, in summary:
- Two `Whole-Word-Masking` weights for Bert (cased and uncased)
- Three Fine-tuned models for Bert (on SQuAD and MRPC)
- One German model for Bert provided and trained by Deepset.ai (tholor and Timoeller) as detailed in their nice [blogpost](https://deepset.ai/german-bert)
- One OpenAI GPT-2 model (medium size model)
- Two models (base and large) for the newly added XLNet model
- Eight models for the newly added XLM model

The [documentation lists all the models with the shortcut names](https://huggingface.co/pytorch-transformers/pretrained_models.html) and we are currently adding full details of the associated pretraining/fine-tuning parameters.

New documentation

New documentation is currently being created at https://huggingface.co/pytorch-transformers/ and should be finalized over the coming days.

Standard API across models
See the [readme](https://github.com/huggingface/pytorch-transformers#quick-tour) for a quick tour of the API.

Main points:

- All models now return `tuples` with various elements depending on the model and the configuration. The docstrings and [documentation](https://huggingface.co/pytorch-transformers/model_doc/bert.html#bertmodel) list all the expected outputs in order.
- All models can now return the full list of hidden-states (embeddings output + the output hidden-states of each layer)
- All models can now return the full list of attention weights (one tensor of attention weights for each layer)

python
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased',
output_hidden_states=True,
output_attentions=True)
input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
all_hidden_states, all_attentions = model(input_ids)[-2:]


Standard API to add tokens to the vocabulary and the model

Using `tokenizer.add_tokens()` and `tokenizer.add_special_tokens()`, one can now easily add tokens to each model vocabulary. The model's input embeddings can be resized accordingly to add associated word embeddings (to be trained) using `model.resize_token_embeddings(len(tokenizer))`

python
tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
model.resize_token_embeddings(len(tokenizer))


Serialization

The serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.

python
model.save_pretrained('./my_saved_model_directory/')
tokenizer.save_pretrained('./my_saved_model_directory/')

Reload the model and the tokenizer
model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')


Torchscript

All models are now compatible with Torchscript.

python
model = model_class.from_pretrained(pretrained_weights, torchscript=True)
traced_model = torch.jit.trace(model, (input_ids,))


Examples scripts

The examples scripts have been refactored and gathered in three main examples (`run_glue.py`, `run_squad.py` and `run_generation.py`) which are common to several models and are designed to offer SOTA performances on the respective tasks while being clean starting point to design your own scripts.

Other examples scripts (like `run_bertology.py`) will be added in the coming weeks.

Breaking-changes

The [migration section](https://github.com/huggingface/pytorch-transformers#migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) of the readme lists the breaking changes when switching from `pytorch-pretrained-bert` to `pytorch-transformers`.

The main breaking change is that all models now returns a `tuple` of results.

General updates:
- Better serialization for all models and tokenizers (BERT, GPT, GPT-2 and Transformer-XL) with [best practices for saving/loading](https://github.com/huggingface/pytorch-pretrained-BERT#serialization-best-practices) in readme and examples.
- Relaxing network connection requirements (fallback on the last downloaded model in the cache when we can't reach AWS to check eTag)

Breaking changes:
- `warmup_linear` method in `OpenAIAdam` and `BertAdam` is now replaced by flexible [schedule classes](https://github.com/huggingface/pytorch-pretrained-BERT#learning-rate-schedules) for linear, cosine and multi-cycles schedules.

Bug fixes and improvements to the library modules:
- add a flag in BertTokenizer to skip basic tokenization (john-hewitt)
- Allow tokenization of sequences > 512 (CatalinVoss)
- clean up and extend learning rate schedules in BertAdam and OpenAIAdam (lukovnikov)
- Update GPT/GPT-2 Loss computation (CatalinVoss, thomwolf)
- Make the TensorFlow conversion tool more robust (marpaia)
- fixed BertForMultipleChoice model init and forward pass (dhpollack)
- Fix gradient overflow in GPT-2 FP16 training (SudoSharma)
- catch exception if pathlib not installed (potatochip)
- Use Dropout Layer in OpenAIGPTMultipleChoiceHead (pglock)

New scripts and improvements to the examples scripts:
- Add BERT language model fine-tuning scripts (Rocketknight1)
- Added SST-2 task and remaining GLUE tasks to 'run_classifier.py' (ananyahjha93, jplehmann)
- GPT-2 generation fixes (CatalinVoss, spolu, dhanajitb, 8enmann, SudoSharma, cynthia)

Add `regex` to the requirements for OpenAI GPT-2 tokenizer.

Add OpenAI small GPT-2 pretrained model

Mostly a bug fix update for loading the `TransfoXLModel` from s3:

* Fixes a bug in the loading of the pretrained `TransfoXLModel` from the s3 dump (which is a converted `TransfoXLLMHeadModel`) in which the weights were not loaded.
* Added a fallback of `OpenAIGPTTokenizer` on BERT's `BasicTokenizer` when SpaCy and ftfy are not installed. Using BERT's `BasicTokenizer` instead of SpaCy should be fine in most cases as long as you have a relatively clean input (SpaCy+ftfy were included to exactly reproduce the paper's pre-processing steps on the Toronto Book Corpus) and this also let us use the `never_split` option to avoid splitting special tokens like `[CLS], [SEP]...` which is easier than adding the tokens after tokenization.
* Updated the README on the tokenizers options and methods which was lagging behind a bit.